Large-scale value-based non-linear models often require large numbers of decision variables and constraints (e.g., over a million), potentially with vastly infeasible search spaces (e.g., discovery of feasible decision variable sets without a vastly infeasible search space mechanism may be relatively difficult or impossible). Multi-objective Evolutionary Algorithms (MOEA) may be particularly useful in optimizing problems with large number of interdependent variables. Often times in these MOEA, the initially determined optimization may not transpire as intended. This may be due to improper implementation of the optimized operation or due to perturbations that are not in the control of an implementer of the optimized solution. In these cases, the implementation of an optimized solution may differ from the optimized solution.